Generating Predicate Callback Summaries for the Android Framework

Perez, Danilo Dominguez; Le, Wei

doi:10.1109/mobilesoft.2017.28

Cited by 10 publications

(10 citation statements)

References 26 publications

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“…Research efforts on understanding callbacks are limited to specific objectives that prevent their use for string analysis in general. Such efforts have included a focus restricted to GUI-related callbacks [46,47] (which we do use in our callback analysis, detailed in Section 3.2), assumption that callback control-flow can be in any arbitrary order [7], and analysis of the Android framework-level, but not app-level, code to construct callback summaries [11,30].…”

Section: String Analysismentioning

confidence: 99%

Leveraging program analysis to reduce user-perceived latency in mobile applications

Zhao

Laser

Lyu

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

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Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel clientcentric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applications. INTRODUCTIONIn mobile computing, user-perceived latency is a critical concern as it directly impacts user experience and often has severe economic consequences. A recent report shows that a majority of mobile users would abandon a transaction or even delete an app if the response time of a transaction exceeds three seconds [6]. Google estimates that an additional 500ms delay per transaction would result in up to 20% loss of traffic, while Amazon estimates that every 100ms delay would cause 1% annual sales loss [42]. A previous study showed that network transfer is often the performance bottleneck, and mobile apps spend 34-85% of their time fetching data from the Internet [32]. A compounding factor is that mobile devices rely on wireless networks, which can exhibit high latency, intermittent connectivity, and low bandwidth [21].Reducing network latency thus becomes a highly effective way of improving the mobile user experience. In the context of mobile communication, we define latency as the response time of an HTTP request. In this paper, we propose a novel client-centric technique for minimizing the network latency by prefetching HTTP requests in mobile apps. Prefetching bypasses the performance bottleneck (in this case, network speed) and masks latency by allowing a response to a request to be generated immediately, from a local cache.

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Section: String Analysismentioning

confidence: 99%

Leveraging program analysis to reduce user-perceived latency in mobile applications

Zhao

Laser

Lyu

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

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“…In Figure 2, we show that our approach to computing CCFAs takes a Windows Transition Graph (WTG) [10], the interprocedural control flow graphs (ICFGs) of callbacks in the app, and the Predicate Callback Summaries (PCSs) of the Android framework [14] as inputs. The WTG contains pre-computed callback control flow related to windows (Activities, Menus or Dialogs) and GUI events, and the PCS specifies the pre-computed callback control flow for the Android framework methods.…”

Section: How To Compute a Ccfamentioning

confidence: 99%

“…Other approaches modeled a subset of callbacks, specifically the callbacks invoked from external events including GUI [10], [11], [12] and sensors' events [13]. Another approach automatically summarize the Android framework to identify the control flow of callbacks implemented in the Android API methods [14]. Nevertheless, there is not a representation that integrates different sources of changes of control, such as external events and API methods, and be directly usable by analysis and testing tools.…”

Section: Introductionmentioning

confidence: 99%

“…To compute CCFAs, we designed a static analysis algorithm that traverses Windows Transition Graph (WTG) [10] to obtain the control flow of callbacks related to the windows (activities, dialogs and menus) and GUI events, and also, integrates Predicate Callback Summaries (PCSs) [14] for callback sequences invoked in the Android API methods. These API methods include the callbacks of the Android components' lifecycles, which are modeled manually in the previous work.…”

Section: Introductionmentioning

confidence: 99%

“…We implemented the construction and applications of CCFAs using Soot and Spark. The WTGs were computed using GATOR [16] and the summaries for Android API methods were provided by Lithium [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Specifying Callback Control Flow of Mobile Apps Using Finite Automata

Perez

2021

IIEEE Trans. Software Eng.

Self Cite

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Given the event-driven and framework-based architecture of Android apps, finding the ordering of callbacks executed by the framework remains a problem that affects every tool that requires inter-callback reasoning. Previous work has focused on the ordering of callbacks related to the Android components and GUI events. But the execution of callbacks can also come from direct calls of the framework (API calls). This paper defines a novel program representation, called Callback Control Flow Automata (CCFA), that specifies the control flow of callbacks invoked via a variety of sources. We present an analysis to automatically construct CCFAs by combining two callback control flow representations developed from the previous research, namely, Window Transition Graphs (WTGs) and Predicate Callback Summaries (PCSs). To demonstrate the usefulness of our representation, we integrated CCFAs into two client analyses: a taint analysis using FLOWDROID, and a value-flow analysis that computes source and sink pairs of a program. Our evaluation shows that we can compute CCFAs efficiently and that CCFAs improved the callback coverages over WTGs. As a result of using CCFAs, we obtained 33 more true positive security leaks than FLOWDROID over a total of 55 apps we have run. With a low false positive rate, we found that 22.76% of source-sink pairs we computed are located in different callbacks and that 31 out of 55 apps contain source-sink pairs spreading across components. Thus, callback control flow graphs and inter-callback analysis are indeed important. Although this paper mainly uses Android, we believe that CCFAs can be useful for modeling control flow of callbacks for other event-driven, framework-based systems.

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